Adversarial Variational Autoencoder for Top-N Recommender Systems
Guijuan Zhang, Yang Liu, Xiaoning Jin · 2018
Recommender systems play an important role in the age of mass information. They allow users to discover items that match their tastes. In this paper, we propose a novel method, called adversarial variational autoencoder, for top-N recommendation. We use generative adversarial networks to regularize variational autoencoder by imposing an arbitrary prior on the latent representation of VAE, which makes the recommendation model. We define a joint objective function as a minimization problem. Our experiments on three datasets show that the proposed model achieves high recommendation accuracy compared to other state-of-the-art models.